Rengo AI AI Engineer

Rengo AI is building the intelligence layer for fund management starting with next-generation portfolio monitoring systems for investment teams.

Today portfolio monitoring is fragmented across dashboards spreadsheets internal tools and manual analyst workflows. Rengo replaces this with an AI-native monitoring layer that continuously interprets portfolio activity risk exposure and performance across assets and strategies.

As a Founding AI Engineer you will build the core system that powers AI-driven portfolio monitoring for institutional investors.

You will design systems that continuously:

• ingest portfolio market position-level data

• detect meaningful changes and anomalies

• generate structured investment insights

• explain performance and risk drivers in natural language structured outputs

This is a high-reliability AI system not a chatbot.

1. AI Portfolio Monitoring Engine

• Real-time and batch systems that monitor:

  • portfolio performance (PnL attribution drawdowns)

  • exposure shifts (sector geography asset class)

  • risk signals (volatility correlation concentration)

  • position-level changes

• AI layer that converts raw portfolio data into:

  • alerts

  • summaries

  • explanations

  • actionable insights
    

2. Change Detection & Intelligence Layer

• Build systems that detect:

  • significant portfolio movements

  • abnormal price/volume behavior in holdings

  • drift from target allocations

  • risk regime changes

• Prioritization layer: what matters vs noise
  

3. AI-Generated Portfolio Narratives

• Generate structured outputs such as:

  • daily / weekly portfolio reports

  • performance explanations (why did we lose/gain)

  • exposure breakdowns

  • risk commentary

• Ensure outputs are:

  • auditable

  • grounded in data

  • consistent across runs
    

4. Data Retrieval Systems for Funds

• Integrate:

  • positions & holdings data

  • market data feeds

  • internal fund metadata

  • external news & filings (optional enrichment layer)

• Build RAG pipelines over portfolio market context

5. LLM Systems for Financial Reliability

• Design LLM pipelines that:

  • avoid hallucinated financial reasoning

  • produce structured verifiable outputs

  • ground insights in actual portfolio data

• Build evaluation frameworks for correctness of financial narratives

• 37 years in backend data engineering or ML systems

• Strong Python (mandatory)

• Experience building production data systems or analytics platforms
  

• Experience building LLM applications in production

• Strong understanding of:

  • RAG systems

  • structured generation (schemas JSON outputs)

  • tool use / function calling

  • agent workflows

• Awareness of failure modes in LLM reasoning (critical in finance)
  

• Experience with:

  • time-series data

  • event-driven pipelines

  • analytics / observability systems

• Comfort working with imperfect high-volume financial data
  

• Experience in:

  • asset management / hedge funds / fintech

  • portfolio analytics or risk systems

  • trading / market data infrastructure

• Familiarity with:

  • exposure/risk models

  • PnL attribution systems

  • BI / analytics platforms for finance

• Experience with vector databases or hybrid retrieval systems

• You are building the core monitoring brain of a fund

• Not dashboards interpretation intelligence

• Systems you build directly influence investment decisions and risk awareness

• High emphasis on:

  • correctness

  • traceability

  • reliability under uncertainty

• You own the full stack: data intelligence insight delivery

• Python (core systems AI orchestration)

• LLM APIs (OpenAI / Anthropic / open-source models)

• Postgres time-series storage

• Vector DB for semantic retrieval

• Stream/batch processing pipelines

• Cloud infrastructure (AWS/GCP)

• Define how AI monitors institutional portfolios

• Replace manual analyst workflows with automated intelligence systems

• Work on one of the hardest AI problems in finance: turning data into trustworthy interpretation

• High ownership early-stage no legacy constraints